India 2023 Weather Predictive Analysis Project

  • Creator : Anandan Raju
  • Source : https://www.ncdc.noaa.gov/cdo-web/search

Introduction¶

Weather forecasting is the use of science and technology to forecast atmospheric conditions for a certain place and period.

Types of Weather Forecasting

  • Short Range Forecasting
  • Medium Range Forecasting
  • Long-Range Forecasts

Weather Forecasting Process

A weather forecast is made up of three steps: observation and analysis, extrapolation to determine the state of the atmosphere in the future, and estimation of specific variables. One method of qualitative extrapolation is to conclude the weather features will continue to travel in the same direction as they have been.

Observation and Analysis¶

  1. While data-access policies differ by country, all of these reports are sent to regional and global centers through the World Meteorological Organization's (WMO) Global Telecommunications System (GTS).

  2. The data is then compiled, redistributed in the GTS, and used in various numerical forecasting models. Typically, these numerical models begin with data collected between the hours of 0000 and 1200 UTC (7 A.M. and 7 P.M. Eastern Standard Time, respectively).

  3. To aid the forecaster, the data is printed, mapped, and graphed in several ways. Furthermore, some "initialization" routines slightly change the data when it enters a prediction model only for that model.

weather%20forecast.jpg

Precipitation :¶

Precipitation is water that falls back to the surface of the earth upon condensing in the atmosphere. In other words, any form of water which falls, whether it is in liquid or frozen form, falls as precipitation only. Now that we are clear on what is precipitation, let us move on to the different types. You can consider heavy or light rain as precipitation and even snow, drizzle and hail.

Types of Precipitation :

Precipitation plays a major part in the water cycle as it is the one which brings in the deposit of freshwater on the planet. It can be divided into three categories depending upon the form such as:

  • Liquid water
  • Ice
  • Liquid water freezes when comes in contact with the surface

Temperature :¶

Temperature, measure of hotness or coldness expressed in terms of any of several arbitrary scales and indicating the direction in which heat energy will spontaneously flow i.e., from a hotter body (one at a higher temperature) to a colder body (one at a lower temperature).

Three temperature scales are in general use today.

  • The Fahrenheit (°F) temperature scale is used in the United States and a few other English-speaking countries.
  • The Celsius (°C) temperature scale is standard in virtually all countries that have adopted the metric system of measurement, and it is widely used in the sciences.
  • The Kelvin (K) scale, an absolute temperature scale (obtained by shifting the Celsius scale by −273.15° so that absolute zero coincides with 0 K), is recognized as the international standard for scientific temperature measurement.

Table of Contents

  1. Import Libraries & Install Packages
  2. Reading and Checking Dataset
  3. Exploratory Data Analysis
  4. Checking for Missing Value
  5. Training ML Model
  6. Evaluate Model
  7. Function to Make Prediction
  8. Addition of Rolling Means
  9. Adding Monthly and Daily Averages
  10. Running Model Diagnostics
  11. GeoMap Creation
  12. Creation of Pandas Profiling Report

1. Import Libraries & Install Packages

Table of contents

In [1]:
pip install tslearn

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pip install jupyter-datatables

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In [3]:
pip install geopy

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In [4]:
pip install folium

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In [5]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import seaborn as sns
import plotly.express as px
import folium

from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from geopy.geocoders import ArcGIS
from pandas_profiling import ProfileReport

from jupyter_datatables import init_datatables_mode

import warnings
warnings.filterwarnings('ignore')

2. Reading and Checking Dataset

Table of contents

In [6]:
weather = pd.read_csv("India_Weather_2008_to_2022.csv",index_col='DATE')
In [7]:
weather.index.is_monotonic
Out[7]:
False
In [8]:
weather.duplicated().value_counts()
Out[8]:
False    155242
True      68958
dtype: int64
In [9]:
weather.head()
Out[9]:
STATION NAME LATITUDE LONGITUDE ELEVATION PRCP TAVG TMAX TMIN
DATE
01-01-2018 IN017111200 BHUBANESWAR, IN 20.250 85.833 46.0 NaN 69 NaN NaN
01-01-2018 IN019040100 BARMER, IN 25.750 71.383 194.0 NaN 65 84.0 51.0
01-01-2018 IN005010600 AHMADABAD, IN 23.067 72.633 55.0 NaN 69 NaN 51.0
01-01-2018 IN005100400 PORBANDAR, IN 21.650 69.667 7.0 NaN 68 85.0 54.0
01-01-2018 IN002050100 PASIGHAT, IN 28.100 95.383 157.0 NaN 69 78.0 49.0
In [10]:
pd.Series(weather['NAME'].value_counts())
Out[10]:
THIRUVANANTHAPURAM, IN    3645
BAREILLY, IN              1823
BHUJ RUDRAMATA, IN        1823
POONA, IN                 1823
PATNA, IN                 1823
                          ... 
SANGLI, IN                 340
ALLAHABAD, IN               49
PARBHANI, IN                 9
CHERRAPUNJI, IN              7
PHALODI, IN                  2
Name: NAME, Length: 137, dtype: int64

3. Exploratory Data Analysis

Table of contents

In [11]:
weather.describe().T
Out[11]:
count mean std min 25% 50% 75% max
LATITUDE 224200.0 21.701109 5.977627 8.467 17.45 22.717 26.233 34.083
LONGITUDE 224200.0 79.776829 6.211600 68.850 75.40 78.250 83.250 95.383
ELEVATION 224200.0 303.068816 406.967568 3.000 38.00 177.000 393.000 2311.000
PRCP 72155.0 0.439194 0.911140 0.000 0.00 0.080 0.470 18.900
TAVG 224200.0 78.979536 10.036296 25.000 74.00 81.000 86.000 110.000
TMAX 118640.0 89.845491 10.091496 35.000 85.00 90.000 96.000 123.000
TMIN 187867.0 68.579032 11.204690 16.000 62.00 72.000 77.000 94.000
In [12]:
weather.hist(bins=20,figsize=(20,18),color='green')
Out[12]:
array([[<AxesSubplot:title={'center':'LATITUDE'}>,
        <AxesSubplot:title={'center':'LONGITUDE'}>,
        <AxesSubplot:title={'center':'ELEVATION'}>],
       [<AxesSubplot:title={'center':'PRCP'}>,
        <AxesSubplot:title={'center':'TAVG'}>,
        <AxesSubplot:title={'center':'TMAX'}>],
       [<AxesSubplot:title={'center':'TMIN'}>, <AxesSubplot:>,
        <AxesSubplot:>]], dtype=object)
In [13]:
sns.jointplot(x=weather.LATITUDE.values, y=weather.LONGITUDE.values, size=13,color='RED')
plt.ylabel('Longitude', fontsize=12)
plt.xlabel('Latitude', fontsize=12)
plt.figure(figsize=(7,7))
plt.show()

<Figure size 700x700 with 0 Axes>
In [14]:
plt.figure(figsize=(10,35))
sns.boxplot(y='NAME',x='PRCP',data=weather,palette='terrain_r')
Out[14]:
<AxesSubplot:xlabel='PRCP', ylabel='NAME'>
In [15]:
plt.figure(figsize=(10,35))
sns.boxplot(y='NAME',x='TMAX',data=weather,palette='YlOrRd')
Out[15]:
<AxesSubplot:xlabel='TMAX', ylabel='NAME'>
In [16]:
plt.figure(figsize=(10,35))
sns.boxplot(y='NAME',x='TMIN',data=weather,palette='rainbow')
Out[16]:
<AxesSubplot:xlabel='TMIN', ylabel='NAME'>
In [17]:
plt.figure(figsize=(10,7))
sns.scatterplot(y='PRCP',x='TMAX',hue='TAVG',data=weather,palette='magma')
Out[17]:
<AxesSubplot:xlabel='TMAX', ylabel='PRCP'>
In [18]:
weather.info()

<class 'pandas.core.frame.DataFrame'>
Index: 224200 entries, 01-01-2018 to 31-12-2022
Data columns (total 9 columns):
 #   Column     Non-Null Count   Dtype  
---  ------     --------------   -----  
 0   STATION    224200 non-null  object 
 1   NAME       224200 non-null  object 
 2   LATITUDE   224200 non-null  float64
 3   LONGITUDE  224200 non-null  float64
 4   ELEVATION  224200 non-null  float64
 5   PRCP       72155 non-null   float64
 6   TAVG       224200 non-null  int64  
 7   TMAX       118640 non-null  float64
 8   TMIN       187867 non-null  float64
dtypes: float64(6), int64(1), object(2)
memory usage: 22.1+ MB
In [19]:
print('Percentage of Null Values :')
weather.apply(pd.isnull).sum()/weather.shape[0]*100

Percentage of Null Values :
Out[19]:
STATION       0.000000
NAME          0.000000
LATITUDE      0.000000
LONGITUDE     0.000000
ELEVATION     0.000000
PRCP         67.816682
TAVG          0.000000
TMAX         47.082962
TMIN         16.205620
dtype: float64
In [20]:
weather1 = weather[["PRCP", "TMAX", "TMIN","LONGITUDE","LATITUDE"]].copy()
weather1.columns = ['precip','temp_max','temp_min','lat','long']
In [21]:
weather1.apply(pd.isnull).sum()/weather1.shape[0]*100
Out[21]:
precip      67.816682
temp_max    47.082962
temp_min    16.205620
lat          0.000000
long         0.000000
dtype: float64
In [22]:
weather1[pd.isnull(weather1['precip'])]
Out[22]:
precip temp_max temp_min lat long
DATE
01-01-2018 NaN NaN NaN 85.833 20.250
01-01-2018 NaN 84.0 51.0 71.383 25.750
01-01-2018 NaN NaN 51.0 72.633 23.067
01-01-2018 NaN 85.0 54.0 69.667 21.650
01-01-2018 NaN 78.0 49.0 95.383 28.100
... ... ... ... ... ...
31-12-2022 NaN NaN 72.0 80.183 13.000
31-12-2022 NaN 74.0 49.0 83.017 25.300
31-12-2022 NaN 92.0 61.0 78.100 18.667
31-12-2022 NaN NaN 43.0 78.033 30.317
31-12-2022 NaN NaN 50.0 85.100 25.600

152045 rows × 5 columns

In [23]:
weather1['precip'].value_counts()/weather1.shape[0]
Out[23]:
0.00     0.101971
0.04     0.016534
0.08     0.016467
0.02     0.015040
0.12     0.013091
           ...   
3.12     0.000004
15.51    0.000004
5.09     0.000004
11.34    0.000004
3.57     0.000004
Name: precip, Length: 588, dtype: float64
In [24]:
weather1['precip'] = weather1['precip'].fillna(0)
In [25]:
weather1.apply(pd.isnull).sum()
Out[25]:
precip           0
temp_max    105560
temp_min     36333
lat              0
long             0
dtype: int64
In [26]:
weather1[pd.isnull(weather1['temp_min'])]
Out[26]:
precip temp_max temp_min lat long
DATE
01-01-2018 0.0 NaN NaN 85.833 20.250
01-01-2018 0.0 68.0 NaN 77.117 28.567
01-01-2018 0.0 NaN NaN 79.950 23.200
01-01-2018 0.0 70.0 NaN 76.883 30.733
01-01-2018 0.0 64.0 NaN 80.883 26.750
... ... ... ... ... ...
31-12-2022 0.0 NaN NaN 77.633 12.950
31-12-2022 0.0 NaN NaN 75.633 15.417
31-12-2022 0.0 84.0 NaN 78.617 25.450
31-12-2022 0.0 NaN NaN 73.917 20.133
31-12-2022 0.0 81.0 NaN 75.850 25.150

36333 rows × 5 columns

In [27]:
weather1.loc['10-10-2022':'20-10-2022']
Out[27]:
precip temp_max temp_min lat long
DATE
10-10-2022 0.00 79.0 73.0 74.600 16.850
10-10-2022 0.16 90.0 74.0 85.833 20.250
10-10-2022 0.00 NaN 76.0 71.383 25.750
10-10-2022 0.00 NaN 78.0 72.633 23.067
10-10-2022 0.00 94.0 NaN 69.667 21.650
... ... ... ... ... ...
20-10-2022 0.00 93.0 66.0 83.017 25.300
20-10-2022 0.00 67.0 45.0 79.650 29.467
20-10-2022 0.00 91.0 75.0 78.100 18.667
20-10-2022 0.00 85.0 NaN 78.033 30.317
20-10-2022 0.00 NaN 72.0 85.100 25.600

1381 rows × 5 columns

In [28]:
weather1 = weather1.fillna(method='ffill')
In [29]:
weather1['temp_max'] = weather1['temp_max'].fillna(0)
weather1.apply(pd.isnull).sum()
Out[29]:
precip      0
temp_max    0
temp_min    1
lat         0
long        0
dtype: int64
In [30]:
weather1['temp_min'] = weather1['temp_min'].fillna(0)
weather1.apply(pd.isnull).sum()
Out[30]:
precip      0
temp_max    0
temp_min    0
lat         0
long        0
dtype: int64

4. Checking for Missing Value

Table of contents

In [31]:
weather1.dtypes
Out[31]:
precip      float64
temp_max    float64
temp_min    float64
lat         float64
long        float64
dtype: object
In [32]:
weather1.index
Out[32]:
Index(['01-01-2018', '01-01-2018', '01-01-2018', '01-01-2018', '01-01-2018',
       '01-01-2018', '01-01-2018', '01-01-2018', '01-01-2018', '01-01-2018',
       ...
       '31-12-2022', '31-12-2022', '31-12-2022', '31-12-2022', '31-12-2022',
       '31-12-2022', '31-12-2022', '31-12-2022', '31-12-2022', '31-12-2022'],
      dtype='object', name='DATE', length=224200)
In [33]:
weather1.index = pd.to_datetime(weather1.index)
weather1.index
Out[33]:
DatetimeIndex(['2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01',
               '2018-01-01', '2018-01-01', '2018-01-01', '2018-01-01',
               '2018-01-01', '2018-01-01',
               ...
               '2022-12-31', '2022-12-31', '2022-12-31', '2022-12-31',
               '2022-12-31', '2022-12-31', '2022-12-31', '2022-12-31',
               '2022-12-31', '2022-12-31'],
              dtype='datetime64[ns]', name='DATE', length=224200, freq=None)
In [34]:
weather1.index.year
Out[34]:
Int64Index([2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018,
            ...
            2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022],
           dtype='int64', name='DATE', length=224200)
In [35]:
weather1.apply(lambda x: (x <= -1).sum())
Out[35]:
precip      0
temp_max    0
temp_min    0
lat         0
long        0
dtype: int64
In [36]:
weather1.apply(lambda x: (x == 9999).sum())
Out[36]:
precip      0
temp_max    0
temp_min    0
lat         0
long        0
dtype: int64
In [37]:
weather1[['temp_max','temp_min']].plot(figsize=(12,6))
Out[37]:
<AxesSubplot:xlabel='DATE'>
In [38]:
weather1[['precip','temp_max','temp_min']].plot(figsize=(12,6))
Out[38]:
<AxesSubplot:xlabel='DATE'>
In [39]:
weather1.index.year.value_counts()
Out[39]:
2022    46285
2020    45123
2019    44916
2021    44696
2018    43180
Name: DATE, dtype: int64
In [40]:
weather1['precip'].plot(figsize=(12,6))
Out[40]:
<AxesSubplot:xlabel='DATE'>
In [41]:
weather1.groupby(weather1.index).apply(lambda x: x["precip"].sum())
Out[41]:
DATE
2018-01-01     0.02
2018-01-02     0.04
2018-01-03     0.04
2018-01-04     0.03
2018-01-05    19.65
              ...  
2022-12-27     4.61
2022-12-28     1.83
2022-12-29     0.06
2022-12-30     0.69
2022-12-31     0.08
Length: 1824, dtype: float64
In [42]:
weather1.groupby(weather1.index).apply(lambda x: x["precip"].sum()).plot(figsize=(12,6))
Out[42]:
<AxesSubplot:xlabel='DATE'>
In [43]:
weather1['temp_max'].plot(figsize=(12,6))
Out[43]:
<AxesSubplot:xlabel='DATE'>
In [44]:
weather1.groupby(weather1.index).apply(lambda x: x["temp_max"].sum()).plot(figsize=(12,6))
Out[44]:
<AxesSubplot:xlabel='DATE'>
In [45]:
weather1['temp_min'].plot(figsize=(12,6))
Out[45]:
<AxesSubplot:xlabel='DATE'>
In [46]:
weather1.groupby(weather1.index).apply(lambda x: x["temp_min"].sum()).plot(figsize=(12,6))
Out[46]:
<AxesSubplot:xlabel='DATE'>

5. Training ML Model

Table of contents

In [47]:
weather1['target'] = weather1.shift(-1)['temp_max']
In [48]:
weather1
Out[48]:
precip temp_max temp_min lat long target
DATE
2018-01-01 0.0 0.0 0.0 85.833 20.250 84.0
2018-01-01 0.0 84.0 51.0 71.383 25.750 84.0
2018-01-01 0.0 84.0 51.0 72.633 23.067 85.0
2018-01-01 0.0 85.0 54.0 69.667 21.650 78.0
2018-01-01 0.0 78.0 49.0 95.383 28.100 91.0
... ... ... ... ... ... ...
2022-12-31 0.0 74.0 49.0 83.017 25.300 57.0
2022-12-31 0.0 57.0 33.0 79.650 29.467 92.0
2022-12-31 0.0 92.0 61.0 78.100 18.667 92.0
2022-12-31 0.0 92.0 43.0 78.033 30.317 92.0
2022-12-31 0.0 92.0 50.0 85.100 25.600 NaN

224200 rows × 6 columns

In [49]:
weather1 = weather1.iloc[:-1,:].copy()
In [50]:
weather1
Out[50]:
precip temp_max temp_min lat long target
DATE
2018-01-01 0.0 0.0 0.0 85.833 20.250 84.0
2018-01-01 0.0 84.0 51.0 71.383 25.750 84.0
2018-01-01 0.0 84.0 51.0 72.633 23.067 85.0
2018-01-01 0.0 85.0 54.0 69.667 21.650 78.0
2018-01-01 0.0 78.0 49.0 95.383 28.100 91.0
... ... ... ... ... ... ...
2022-12-31 0.0 90.0 72.0 80.183 13.000 74.0
2022-12-31 0.0 74.0 49.0 83.017 25.300 57.0
2022-12-31 0.0 57.0 33.0 79.650 29.467 92.0
2022-12-31 0.0 92.0 61.0 78.100 18.667 92.0
2022-12-31 0.0 92.0 43.0 78.033 30.317 92.0

224199 rows × 6 columns

In [51]:
# importing libraries
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# loading boston dataset
X = weather1.iloc[:,:5]
y = weather1.target

print ("Weather dataset keys : \n", weather1.keys())


# scaling the inputs
scaler = StandardScaler()
scaled_X = scaler.fit_transform(X)

Weather dataset keys : 
 Index(['precip', 'temp_max', 'temp_min', 'lat', 'long', 'target'], dtype='object')
In [52]:
# Train Test split will be used for both models
X_train, X_test, y_train, y_test = train_test_split(scaled_X, y,train_size = 0.7)
In [53]:
# training model with 0.5 alpha value
model = Ridge(alpha = 0.5, normalize = False, tol = 0.001, solver ='auto', random_state = 42)
model.fit(X_train, y_train)

# predicting the y_test
y_pred = model.predict(X_test)

# finding score for our model
score = model.score(X_test, y_test)
print("Model score : ", score)

Model score :  0.4437244320705207
In [54]:
from sklearn.metrics import mean_absolute_error

print('Mean Absolute Error :',mean_absolute_error(y_test,y_pred))

Mean Absolute Error : 5.173093526222683

6. Evaluate Model

Table of contents

In [55]:
print(X_test,'\n\n')
print(X_train)

[[-0.25416542 -0.47915248 -2.21291314 -0.79590011  1.83449364]
 [ 0.10546554 -1.27698539 -0.15217094 -0.34512983 -1.46397543]
 [ 0.30326256  0.51813865  0.65420644  0.55914754  0.24523279]
 ...
 [-0.25416542 -0.17996515 -0.60015837  0.02788257  0.25075338]
 [ 0.30326256 -1.17725628  0.20621901 -0.32098142 -0.12849444]
 [-0.25416542 -0.5788816   0.74380393 -0.44172346 -0.16747315]] 


[[-0.25416542 -0.27969426  1.10219388  0.8569779   0.65225086]
 [ 1.31022922  0.21895131  1.10219388  1.39629234  0.15874355]
 [-0.25416542  1.01678421  0.65420644 -0.95946532 -1.0402283 ]
 ...
 [-0.25416542 -0.08023603  0.11662152  0.01178363 -1.82917084]
 [-0.25416542 -0.17996515  1.10219388  0.39542134 -0.79481296]
 [-0.21820232 -0.67861071  0.29581649 -0.32098142 -0.12849444]]
In [56]:
print(y_train,'\n\n')
print(y_test)

DATE
2019-07-31     87.0
2022-07-13    103.0
2021-03-31    106.0
2018-02-25     96.0
2019-09-13     87.0
              ...  
2022-09-01     67.0
2022-03-11     90.0
2021-02-28     67.0
2019-08-29     88.0
2018-08-23     90.0
Name: target, Length: 156939, dtype: float64 


DATE
2020-10-02     85.0
2019-02-12     77.0
2021-06-28     95.0
2019-12-05     97.0
2018-03-04     90.0
              ...  
2018-05-15    104.0
2018-10-07     96.0
2020-01-03     88.0
2019-09-13     78.0
2021-07-18     84.0
Name: target, Length: 67260, dtype: float64
In [57]:
combined = pd.concat([y_test,pd.Series(y_pred,index=y_test.index)],axis=1)
combined.columns = ['actual','predictions']
In [58]:
combined
Out[58]:
actual predictions
DATE
2020-10-02 85.0 85.380061
2019-02-12 77.0 80.985598
2021-06-28 95.0 93.967478
2019-12-05 97.0 91.402071
2018-03-04 90.0 90.099724
... ... ...
2018-05-15 104.0 92.878751
2018-10-07 96.0 89.405594
2020-01-03 88.0 88.048292
2019-09-13 78.0 83.142486
2021-07-18 84.0 87.186372

67260 rows × 2 columns

In [59]:
combined.plot(figsize=(12,8))
Out[59]:
<AxesSubplot:xlabel='DATE'>
In [60]:
model.coef_
Out[60]:
array([ 0.28430697,  5.86939596,  1.3365225 , -0.01683403,  0.77628488])

7. Function to Make Prediction

Table of contents

In [61]:
def create_predictions(predictors,weather1,model):
    # training model with 0.5 alpha value
    model = Ridge(alpha = 0.5, normalize = False, tol = 0.001, solver ='auto', random_state = 42)
    model.fit(X_train, y_train)

    # predicting the y_test
    y_pred = model.predict(X_test)
      
    error = mean_absolute_error(y_test,y_pred)
    
    combined = pd.concat([y_test,pd.Series(y_pred,index=y_test.index)],axis=1)
    combined.columns = ['actual','predictions']
    
    return error, combined

8. Addition of Rolling Means

Table of contents

In [62]:
weather1['month_max'] = weather1['temp_max'].rolling(30).mean()
weather1['month_day_max'] = weather1['month_max'] / weather1['temp_max']
weather1['max_min'] = weather1['temp_max'] / weather1['temp_min']
In [63]:
predictors = ['precip','temp_max','temp_min','month_day_max','max_min']
In [64]:
weather1 = weather1.iloc[30:,:].copy()
In [65]:
error,combined = create_predictions(predictors,weather1,model)

print('Mean Absolute Error :',error)

Mean Absolute Error : 5.173093526222683
In [66]:
combined.plot(figsize=(12,8))
Out[66]:
<AxesSubplot:xlabel='DATE'>

9. Adding Monthly and Daily Averages

Table of contents

In [67]:
weather1['monthly_avg'] = weather1['temp_max'].groupby(weather1.index.month).apply(lambda x:x.expanding(1).mean())
weather1["day_of_year_avg"] = weather1["temp_max"].groupby(weather1.index.dayofyear).apply(lambda x: x.expanding(1).mean())
In [68]:
weather1
Out[68]:
precip temp_max temp_min lat long target month_max month_day_max max_min monthly_avg day_of_year_avg
DATE
2018-01-01 0.0 83.0 48.0 72.200 24.200 71.0 78.800000 0.949398 1.729167 83.000000 83.000000
2018-01-01 0.0 71.0 41.0 74.797 31.710 71.0 78.366667 1.103756 1.731707 77.000000 77.000000
2018-01-01 0.0 71.0 66.0 77.050 11.033 70.0 77.933333 1.097653 1.075758 75.000000 75.000000
2018-01-01 0.0 70.0 66.0 76.883 30.733 70.0 77.433333 1.106190 1.060606 73.750000 73.750000
2018-01-01 0.0 70.0 51.0 74.617 15.850 85.0 77.166667 1.102381 1.372549 73.000000 73.000000
... ... ... ... ... ... ... ... ... ... ... ...
2022-12-31 0.0 90.0 72.0 80.183 13.000 74.0 80.233333 0.891481 1.250000 83.046172 76.996800
2022-12-31 0.0 74.0 49.0 83.017 25.300 57.0 80.533333 1.088288 1.510204 83.045699 76.992013
2022-12-31 0.0 57.0 33.0 79.650 29.467 92.0 80.266667 1.408187 1.727273 83.044339 76.960128
2022-12-31 0.0 92.0 61.0 78.100 18.667 92.0 80.933333 0.879710 1.508197 83.044807 76.984076
2022-12-31 0.0 92.0 43.0 78.033 30.317 92.0 81.600000 0.886957 2.139535 83.045274 77.007949

224169 rows × 11 columns

In [69]:
predictors = ['precip','temp_max','temp_min','month_day_max','max_min','monthly_avg','day_of_year_avg']
In [70]:
error, combined = create_predictions(predictors, weather1, model)
print('Mean Absolute Error :',error)

Mean Absolute Error : 5.173093526222683

10. Running Model Diagnostics

Table of contents

In [71]:
model.coef_
Out[71]:
array([ 0.28430697,  5.86939596,  1.3365225 , -0.01683403,  0.77628488])
In [72]:
weather1.corr()["target"]
Out[72]:
precip             0.014176
temp_max           0.656476
temp_min           0.461343
lat               -0.021351
long              -0.024699
target             1.000000
month_max          0.633011
month_day_max     -0.297553
max_min           -0.054600
monthly_avg        0.378183
day_of_year_avg    0.608251
Name: target, dtype: float64
In [73]:
combined["diff"] = (combined["actual"] - combined["predictions"]).abs()
In [74]:
combined.sort_values("diff", ascending=False).head(10)
Out[74]:
actual predictions diff
DATE
2022-01-23 35.0 94.049458 59.049458
2020-01-29 42.0 91.562400 49.562400
2020-08-01 39.0 86.819988 47.819988
2022-09-01 43.0 90.326752 47.326752
2022-01-23 43.0 89.746736 46.746736
2019-08-02 42.0 87.920526 45.920526
2022-01-14 47.0 92.796183 45.796183
2022-12-01 45.0 90.095537 45.095537
2019-01-28 42.0 86.869576 44.869576
2019-01-23 44.0 88.489112 44.489112
In [75]:
plt.figure(figsize=(10,7))
sns.scatterplot(y='predictions',x='actual',hue='diff',data=combined[:10000],palette='rainbow')
Out[75]:
<AxesSubplot:xlabel='actual', ylabel='predictions'>
In [76]:
combined.plot(figsize=(13,8))
Out[76]:
<AxesSubplot:xlabel='DATE'>
In [77]:
plt.figure(figsize=(12,8))
sns.heatmap(weather1.corr(),cmap='viridis',annot=True,linecolor ='white', linewidths = 1)
Out[77]:
<AxesSubplot:>
In [78]:
plt.figure(figsize=(12,5))
sns.heatmap(combined.corr(),cmap='viridis',annot=True,linecolor ='white', linewidths = 1)
Out[78]:
<AxesSubplot:>
In [93]:
submission = pd.DataFrame(combined)
submission.to_csv("Submission.csv")
print("Output of 'India Weather Predicted Model Data' is Converted into CSV file")

Output of 'India Weather Predicted Model Data' is Converted into CSV file
In [105]:
from jupyter_datatables import init_datatables_mode
init_datatables_mode()
In [106]:
Subs = pd.read_csv("India 2023 Weather Predicted Data.csv")
Subs
Out[106]:
In [107]:
list_ = []
for i in y_pred[:10]:
    reg = Ridge(alpha=0.1)
    reg

    reg.fit(combined,y_pred)
    reg.score(combined,y_pred)
    
    reg.predict([[i,i,i]])
    
    list_.append(reg.predict([[i,i,i]]))
    
    
list_ = np.array(list_)
list_ = list_.tolist()
                 
print(list_)

[[85.38006140932755], [80.98559829337285], [93.96747807168816], [91.40207143641906], [90.09972435460296], [99.04614358144414], [96.54358877613576], [100.9839960300573], [107.22107438653101], [89.22818750984308]]
In [108]:
combined_ = combined.merge(weather,left_on=['actual','predictions'],right_on=['TMAX','PRCP'],how='left')
combined_.drop(weather[2:],axis=1,inplace=True)
In [109]:
combined_
Out[109]:

11. GeoMap Creation

Table of contents

In [110]:
from geopy.geocoders import ArcGIS
import folium
In [111]:
nom=ArcGIS()
In [112]:
a_list1 = weather[['NAME','LATITUDE','LONGITUDE']].values.tolist()
In [113]:
a_list = a_list1[:100]
a_list
Out[113]:
[['BHUBANESWAR, IN', 20.25, 85.833],
 ['BARMER, IN', 25.75, 71.383],
 ['AHMADABAD, IN', 23.066999999999997, 72.633],
 ['PORBANDAR, IN', 21.65, 69.667],
 ['PASIGHAT, IN', 28.1, 95.383],
 ['MANGALORE BAJPE, IN', 12.917, 74.883],
 ['BEGUMPET OBSERVATORY, IN', 17.45, 78.47],
 ['RAJKOT, IN', 22.3, 70.783],
 ['SURAT, IN', 21.2, 72.833],
 ['RATNAGIRI, IN', 16.983, 73.333],
 ['BAHRAICH, IN', 27.566999999999997, 81.6],
 ['GWALIOR, IN', 26.233, 78.25],
 ['SRINAGAR, IN', 34.083, 74.833],
 ['KARWAR, IN', 14.783, 74.133],
 ['NEW DELHI PALAM, IN', 28.566999999999997, 77.117],
 ['VISHAKHAPATNAM, IN', 17.717, 83.23299999999999],
 ['NEW DELHI SAFDARJUN, IN', 28.583000000000002, 77.2],
 ['TEZPUR, IN', 26.616999999999997, 92.78299999999999],
 ['BHAUNAGAR, IN', 21.75, 72.2],
 ['PATIALA, IN', 30.333000000000002, 76.467],
 ['CHURU, IN', 28.25, 74.917],
 ['UDAIPUR DABOK, IN', 24.616999999999997, 73.883],
 ['AURANGABAD CHIKALTH, IN', 19.85, 75.4],
 ['JABALPUR, IN', 23.2, 79.95],
 ['SHILLONG, IN', 25.566999999999997, 91.883],
 ['JHARSUGUDA, IN', 21.916999999999998, 84.083],
 ['CHANDBALI, IN', 20.783, 86.73299999999999],
 ['JAISALMER, IN', 26.9, 70.917],
 ['BULDANA, IN', 20.533, 76.233],
 ['NALIYA, IN', 23.25, 68.85],
 ['DEESA, IN', 24.2, 72.2],
 ['AMRITSAR, IN', 31.71, 74.797],
 ['COIMBATORE PEELAMED, IN', 11.033, 77.05],
 ['CHANDIGARH IAFB, IN', 30.733, 76.883],
 ['BELGAUM SAMBRA, IN', 15.85, 74.617],
 ['SAMBALPUR, IN', 21.467, 83.96700000000001],
 ['PAMBAN, IN', 9.267000000000001, 79.3],
 ['LUCKNOW AMAUSI, IN', 26.75, 80.883],
 ['THIRUVANANTHAPURAM, IN', 8.482999999999999, 76.95],
 ['TIRUCHCHIRAPALLI, IN', 10.767000000000001, 78.717],
 ['MAHABALESHWAR, IN', 17.933, 73.667],
 ['GAYA, IN', 24.75, 84.95],
 ['HONAVAR, IN', 14.283, 74.45],
 ['KOZHIKODE, IN', 11.25, 75.783],
 ['BHOPAL BAIRAGARH, IN', 23.283, 77.35],
 ['AKOLA, IN', 20.7, 77.033],
 ['BHUJ RUDRAMATA, IN', 23.25, 69.667],
 ['BAREILLY, IN', 28.366999999999997, 79.4],
 ['VIJAYAWADA GANNAVA, IN', 16.533, 80.8],
 ['HISSAR, IN', 29.166999999999998, 75.733],
 ['KARAIKAL, IN', 10.917, 79.833],
 ['CHITRADURGA, IN', 14.232999999999999, 76.433],
 ['MACHILIPATNAM, IN', 16.2, 81.15],
 ['SATNA, IN', 24.566999999999997, 80.833],
 ['DIBRUGARH MOHANBAR, IN', 27.483, 95.01700000000001],
 ['NAGPUR SONEGAON, IN', 21.1, 79.05],
 ['PURNEA, IN', 25.267, 87.46700000000001],
 ['BOMBAY SANTACRUZ, IN', 19.117, 72.85],
 ['DALTONGANJ, IN', 24.05, 84.06700000000001],
 ['CUDDALORE, IN', 11.767000000000001, 79.767],
 ['GOA PANJIM, IN', 15.482999999999999, 73.817],
 ['AMRAOTI, IN', 20.933000000000003, 77.783],
 ['RAMGUNDAM, IN', 18.767, 79.433],
 ['DHUBRI, IN', 26.017, 89.98299999999999],
 ['AGARTALA, IN', 23.883000000000003, 91.25],
 ['RAIPUR, IN', 21.217, 81.667],
 ['KAILASHAHAR, IN', 24.316999999999997, 92.0],
 ['CALCUTTA ALIPORE, IN', 22.533, 88.333],
 ['AJMER, IN', 26.45, 74.617],
 ['GOPALPUR, IN', 19.267, 84.883],
 ['MADURAI, IN', 9.833, 78.083],
 ['HOSHANGABAD, IN', 22.767, 77.767],
 ['SAGAR, IN', 23.85, 78.75],
 ['AMBIKAPUR, IN', 23.166999999999998, 83.25],
 ['GORAKHPUR, IN', 26.75, 83.367],
 ['CHANDRAPUR, IN', 19.967, 79.3],
 ['BALASORE, IN', 21.517, 86.93299999999999],
 ['SHIMLA, IN', 31.1, 77.167],
 ['THIRUVANANTHAPURAM, IN', 8.467, 76.95],
 ['GANGANAGAR, IN', 29.916999999999998, 73.917],
 ['GAUHATI, IN', 26.1, 91.583],
 ['CALCUTTA DUM DUM, IN', 22.65, 88.45],
 ['PENDRA ROAD, IN', 22.767, 81.9],
 ['NORTH LAKHIMPUR, IN', 27.233, 94.117],
 ['GONDIA, IN', 21.467, 80.2],
 ['JAIPUR SANGANER, IN', 26.816999999999997, 75.8],
 ['VARANASI BABATPUR, IN', 25.45, 82.867],
 ['GUNA, IN', 24.65, 77.317],
 ['SHOLAPUR, IN', 17.667, 75.9],
 ['AMBALA, IN', 30.383000000000003, 76.767],
 ['INDORE, IN', 22.717, 75.8],
 ['PET JORHAT, IN', 26.9, 94.2],
 ['BANGALORE HINDUSTAN, IN', 12.95, 77.633],
 ['JAMMU, IN', 32.667, 74.833],
 ['POONA, IN', 18.533, 73.85],
 ['NAGAPPATTINAM, IN', 10.767000000000001, 79.85],
 ['BANGALORE, IN', 12.967, 77.583],
 ['JODHPUR, IN', 26.3, 73.017],
 ['NELLORE, IN', 14.45, 79.983],
 ['GADAG, IN', 15.417, 75.633]]
In [114]:
weather_map = folium.Map(location=[11.033,77.05])
fg = folium.FeatureGroup(name='weather')

for i in a_list:
    fg.add_child(folium.Marker(location = [i[1],i[2]],popup=i[0],icon=folium.Icon(color='red'),icon_size=0.5))
weather_map.add_child(fg)
Out[114]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [115]:
weather_map.save('India Weather 2008-2022 Visualisation on Location.html')

12. Creation of Pandas Profiling Report

Table of contents

In [116]:
from pandas_profiling import ProfileReport

profile = ProfileReport(weather, title="Pandas Profiling Report")
profile.to_notebook_iframe()

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
In [117]:
profile.to_file("India 2022 Weather Pandas Profiling Report - Anandan Raju.html")

Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]
In [ ]: